Compare AI models
| Specifications | ||
ParametersA rough measure of how big the model is. More parameters usually means more capable and more expensive to run, though it is a poor guide on its own — a smaller, newer model often beats a larger, older one. | 72B | 120B |
Context windowHow much text the model can hold in mind at once — your question, any documents you attach, the conversation so far, and its own reply. Go past it and the earliest part falls out of view. | — | 1M |
| These models have no shared benchmark scores. | ||
SWE-Bench ProAgentic coding — Can the AI fix real bugs in real software? It's handed actual problems from open-source projects and has to write code that genuinely solves them. Higher is better. | — | 51.78% |
SWE-Bench VerifiedCoding — Real coding tasks pulled from open-source projects — the AI has to find and fix actual bugs. A human-checked version of the original SWE-Bench. Higher is better. | — | 71.1% |
SWE-Bench MultilingualMultilingual coding — Like SWE-Bench, but the coding problems span many programming languages, not just one. Tests how broadly the AI can code. Higher is better. | — | 67.72% |
LiveCodeBenchCompetitive coding — Coding problems published so recently the AI can't have seen them in training — a contamination-free test of raw programming skill. Higher is better. | — | 88.22% |
Terminal-Bench 2.1Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Higher is better. | — | 66.7% |
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | — | 78% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 89.52% |
IFBenchInstruction following — Tests whether the AI can follow detailed instructions and satisfy multiple constraints at once. Higher is better. | — | 70.86% |
| Overview | ||
| Company | Moonshot AI | NVIDIA |
| Release date | Jun 17 2025 | Oct 15 2026 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
Kimi-DevvsClaude Haiku 5.5Nemotron 3.5 Super VLvsClaude Haiku 5.5Kimi-DevvsGPT-6.1 SolNemotron 3.5 Super VLvsGPT-6.1 SolKimi-DevvsGemini 4 ArgonNemotron 3.5 Super VLvsGemini 4 ArgonKimi-DevvsMuse Spark OpenNemotron 3.5 Super VLvsMuse Spark OpenKimi-DevvsGrok 4.7Nemotron 3.5 Super VLvsGrok 4.7Kimi-DevvsDeepSeek-V4.1-FlashNemotron 3.5 Super VLvsDeepSeek-V4.1-FlashFrequently asked questions
Kimi-Dev and Nemotron 3.5 Super VL don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Kimi-Dev shipped 485 days before Nemotron 3.5 Super VL, so benchmark comparisons should account for the intervening progress.
Kimi-Dev has 72B parameters, while Nemotron 3.5 Super VL has 120B.
Direct benchmark comparisons are unavailable — Kimi-Dev and Nemotron 3.5 Super VL don't publish scores on any of the same benchmarks.